| 主讲人 |
孙佳婧 |
简介 |
<p>A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit root and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to the aforementioned irregularities. We develop an adjusted-range based Kolmogorov-Smirnov type test for structural breaks in mean for both univariate and multivariate time series and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the "better size but less power" phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.</p> |
